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Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning

2024-04-07 15:03:46
Aurélie Beaufrère, Nora Ouzir, Paul Emile Zafar, Astrid Laurent-Bellue, Miguel Albuquerque, Gwladys Lubuela, Jules Grégory, Catherine Guettier, Kévin Mondet, Jean-Christophe Pesquet, Valérie Paradis


The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Our model identified specific features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a slide could facilitate the diagnosis of primary liver cancers, particularly cHCC-CCA. Method and results: 166 PLC biopsies were divided into training, internal and external validation sets: 90, 29 and 47 samples. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, 256x256 pixel tiles were extracted from the WSIs and used to train a ResNet18. The network was used to extract new tile features. An unsupervised clustering algorithm was then applied to the new tile features. In a two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (Cluster 0: 5-97%; Cluster 1: 2-94%).

Abstract (translated)

原发性肝癌(PLCs)的诊断可能具有挑战性,尤其是在活检和联合肝细胞-胆管癌(cHCC-CCA)的情况下。我们使用弱监督学习方法对常规染色活检中的PLC进行自动分类。弱肿瘤/非肿瘤注释充当训练Resnet18神经网络的标签,网络的最后一卷积层用于提取新的肿瘤拓扑特征。在没有肿瘤的准确标签的情况下,我们 then 应用了无监督聚类算法。我们的模型识别出了肝细胞癌(HCC)和肝内胆管癌(iCCA)的特定特征。尽管没有识别到cHCC-CCA的特定特征,但在同一张幻灯片中检测到HCC和iCCA的肿瘤和正常组织片段可以帮助早期诊断原发性肝癌,特别是cHCC-CCA。方法与结果:166个PLC活检样本分为训练、内部和外部验证集:90、29和47个样本。两名肝病学家审查了每个整个幻灯片的苏丹黑(HES)染色图像(WSI)。在对肿瘤/非肿瘤区域进行标注后,从WSIs中提取了256x256像素的瓷砖用于训练Resnet18。网络用于提取新的瓷砖特征。然后应用无监督聚类算法对新的瓷砖特征进行聚类。在双聚类模型中,Cluster 0和1包含主要HCC和iCCA的病理组织学特征。在内部和外部验证集上,病理诊断与模型预测之间的诊断一致性分别为100%(11/11)和96%(25/26),HCC和iCCA分别为78%(7/9)和87%(13/15)。对于cHCC-CCA,我们观察到每个簇中瓷砖的比例高度变异性(Cluster 0:5-97%;Cluster 1:2-94%)。



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